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Meta-ANN – A dynamic artificial neural network refined by meta-learning for Short-Term Load Forecasting

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  • Xiao, Xun
  • Mo, Huadong
  • Zhang, Yinan
  • Shan, Guangcun

Abstract

In this paper a dynamic Artificial Neural Network (ANN) model called Meta-ANN is developed for forecasting the short-term grid load. The primary ingredient of the model is a base module which is an ANN trained over a large historical data set to learn the long-term trend and seasonality of grid load. To capture the nonstationary pattern of the grid load, an error-correction module based on the idea of meta-learning is integrated into the model. This module finetunes the base module according to most recent prediction errors. For each day of interest, Meta-ANN generates a new ANN model started from the base module by tracing the gradient of the prediction loss on recent observations weighted by learning rates with specific structures. The full Meta-ANN model is trained by jointly optimizing the base module and error-correction module via gradient descent algorithms. The implementation based on gradient descent algorithms is detailed with streamlined mathematical formulations. The proposed model is tested on the open-access data from Elia, a Belgian transmission system operator, for forecasting the daily mean load and load profile. The numerical study shows that Meta-ANN makes more accurate and robust prediction by effectively capturing the nonstationary pattern in grid loads.

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  • Xiao, Xun & Mo, Huadong & Zhang, Yinan & Shan, Guangcun, 2022. "Meta-ANN – A dynamic artificial neural network refined by meta-learning for Short-Term Load Forecasting," Energy, Elsevier, vol. 246(C).
    • Handle: RePEc:eee:energy:v:246:y:2022:i:c:s0360544222003218
    DOI: 10.1016/j.energy.2022.123418
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    2. Wei, Nan & Yin, Chuang & Yin, Lihua & Tan, Jingyi & Liu, Jinyuan & Wang, Shouxi & Qiao, Weibiao & Zeng, Fanhua, 2024. "Short-term load forecasting based on WM algorithm and transfer learning model," Applied Energy, Elsevier, vol. 353(PA).
    3. Gao, Tian & Niu, Dongxiao & Ji, Zhengsen & Sun, Lijie, 2022. "Mid-term electricity demand forecasting using improved variational mode decomposition and extreme learning machine optimized by sparrow search algorithm," Energy, Elsevier, vol. 261(PB).
    4. Huang, Congzhi & Yang, Mengyuan, 2023. "Memory long and short term time series network for ultra-short-term photovoltaic power forecasting," Energy, Elsevier, vol. 279(C).
    5. Pinheiro, Marco G. & Madeira, Sara C. & Francisco, Alexandre P., 2023. "Short-term electricity load forecasting—A systematic approach from system level to secondary substations," Applied Energy, Elsevier, vol. 332(C).
    6. Türkoğlu, A. Selim & Erkmen, Burcu & Eren, Yavuz & Erdinç, Ozan & Küçükdemiral, İbrahim, 2024. "Integrated Approaches in Resilient Hierarchical Load Forecasting via TCN and Optimal Valley Filling Based Demand Response Application," Applied Energy, Elsevier, vol. 360(C).
    7. Eren, Yavuz & Küçükdemiral, İbrahim, 2024. "A comprehensive review on deep learning approaches for short-term load forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PB).

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